RNAseq in embryonic skin of Shh, fgf20, edar mutant mice
CCBR-620
Maggie Cam
Oct 15, 2015
Background
To detect different gene expression in a small subpopulation (<1%) of skin, call Merkel cell, upon shh, fgf20, and edar mutation. Aim to analyze the connection between the three different pathways, and try to get clue of the genes participating in Merkel cell development.
E15.5 embryo skin from 2 mutant lines:
edar mutant = mutation in gene ectodysplasin-A receptor, Single point mutation (Mutation details: This allele involves a G to A transition mutation at nucleotide 1,135 that causes the amino acid change: glutamate to lysine at position 379 (E379K). (J:56496))
phenotype (http://www.informatics.jax.org/allele/allgenoviews/MGI:1856018) = abnormal coat/hair morphology, darkened coat color
fgf20 mutant = fgf20-β-galactosidase knock-in allele phenotype= no guard hair in adult mouse back skin
Data Processing
## Bioconductor version 3.2 (BiocInstaller 1.20.0), ?biocLite for helpThis part of the program was run on biowulf, and data transferred to a local directory
#Command line version
module load subread
x=$(ls *.bam)
featureCounts -p -T 8 -s 2 -p -t exon -g gene_id -a /data/maggiec/RNASeq/Genomes/mm10/gencode.vM4.all.gtf -o counts_ss.txt $x
#Used R version:
gtf="data/maggiec/RNASeq/Genomes/mm10/gencode.vM4.all.gtf"
targets <- readTargets()
fc <- featureCounts(files=targets$bam,isGTFAnnotationFile=TRUE,nthreads=32,
annot.ext=gtf,GTF.attrType="gene_name",strandSpecific=2,isPairedEnd=TRUE)
x <- DGEList(counts=fc$counts, genes=fc$annotation)Load data from local directory:
setwd("/Users/maggiec/GitHub/Maggie/ccbr620/")
#load("data/ssData.RData")
#fc=fc_ss
load("data/RNASeq_orig.RData")
ls()## [1] "fc" "gtf" "targets"#Change the Phenotype (switched)
targets$Phenotype[c(7,8,10)] <- "fgf20lz/lz mutant"
targets$Phenotype[c(9,11,12)] <- "fgf20lz/+ control"
targets## bam Phenotype Batch
## 1 Sample_e10.bam edar/+ control 1
## 2 Sample_e1.bam edar/+ control 2
## 3 Sample_e2.bam edar/+ control 2
## 4 Sample_e4.bam edar/edar mutant 2
## 5 Sample_e5.bam edar/edar mutant 2
## 6 Sample_e9.bam edar/edar mutant 1
## 7 Sample_f1.bam fgf20lz/lz mutant 3
## 8 Sample_f2.bam fgf20lz/lz mutant 3
## 9 Sample_f4.bam fgf20lz/+ control 3
## 10 Sample_f5.bam fgf20lz/lz mutant 3
## 11 Sample_f6.bam fgf20lz/+ control 3
## 12 Sample_f7.bam fgf20lz/+ control 3fc$stat## Status Sample_e10.bam Sample_e1.bam Sample_e2.bam
## 1 Assigned 30137063 38131187 38018334
## 2 Unassigned_Ambiguity 338418 423143 427446
## 3 Unassigned_MultiMapping 5540811 6724855 7144799
## 4 Unassigned_NoFeatures 2332980 2896013 3515618
## 5 Unassigned_Unmapped 0 0 0
## 6 Unassigned_MappingQuality 0 0 0
## 7 Unassigned_FragementLength 0 0 0
## 8 Unassigned_Chimera 0 0 0
## 9 Unassigned_Secondary 0 0 0
## 10 Unassigned_Nonjunction 0 0 0
## 11 Unassigned_Duplicate 0 0 0
## Sample_e4.bam Sample_e5.bam Sample_e9.bam Sample_f1.bam Sample_f2.bam
## 1 39859361 37639209 21918421 29010380 31527017
## 2 464930 429850 251806 328766 361974
## 3 7824053 7377360 4328405 5049329 5805782
## 4 3938754 3294804 1846191 3223421 3212604
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## 7 0 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## Sample_f4.bam Sample_f5.bam Sample_f6.bam Sample_f7.bam
## 1 20773860 30420667 32484170 31229089
## 2 226857 343845 360251 353216
## 3 3820429 5435442 5666179 5505908
## 4 1941022 3457653 3716923 3263560
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0fc1=mat=fc$counts
tfc1=t(fc1)
filter <- apply(fc1, 1, function(x) length(x[x>5])>=1)
fc1filt <- fc1[filter,]
genes <- rownames(fc1filt)QC Check: Look at raw signal distribution and median expression levels
Data is normalized by TMM and quantile normalization
## [1] 16253 12
Run Voom
#Do analysis for entire group
celltype <- factor(targets$Phenotype)
Batch <- factor(targets$Batch)
cell_rep=paste(celltype,Batch,sep=".")
design <- model.matrix(~0+celltype)
design## celltypeedar/+ control celltypeedar/edar mutant
## 1 1 0
## 2 1 0
## 3 1 0
## 4 0 1
## 5 0 1
## 6 0 1
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## celltypefgf20lz/+ control celltypefgf20lz/lz mutant
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 1
## 8 0 1
## 9 1 0
## 10 0 1
## 11 1 0
## 12 1 0
## attr(,"assign")
## [1] 1 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$celltype
## [1] "contr.treatment"dev.off()## null device
## 1v <- voom(x,design,plot=TRUE,normalize="quantile")After Normalization
## Using as id variables
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Preprocessing of edar group (voom): 2 batches of pups (voom)
## bam Phenotype Batch
## 1 Sample_e10.bam edar/+ control 1
## 2 Sample_e1.bam edar/+ control 2
## 3 Sample_e2.bam edar/+ control 2
## 4 Sample_e4.bam edar/edar mutant 2
## 5 Sample_e5.bam edar/edar mutant 2
## 6 Sample_e9.bam edar/edar mutant 1## (Intercept) Batch2 celltypeedar/edar mutant
## 1 1 0 0
## 2 1 1 0
## 3 1 1 0
## 4 1 1 1
## 5 1 1 1
## 6 1 0 1
## attr(,"assign")
## [1] 0 1 2
## attr(,"contrasts")
## attr(,"contrasts")$Batch
## [1] "contr.treatment"
##
## attr(,"contrasts")$celltype
## [1] "contr.treatment"
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Preprocessing of Fgf group (voom): 1 batch of pups
## bam Phenotype Batch
## 7 Sample_f1.bam fgf20lz/lz mutant 3
## 8 Sample_f2.bam fgf20lz/lz mutant 3
## 9 Sample_f4.bam fgf20lz/+ control 3
## 10 Sample_f5.bam fgf20lz/lz mutant 3
## 11 Sample_f6.bam fgf20lz/+ control 3
## 12 Sample_f7.bam fgf20lz/+ control 3## (Intercept) celltypefgf20lz/lz mutant
## 1 1 1
## 2 1 1
## 3 1 0
## 4 1 1
## 5 1 0
## 6 1 0
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$celltype
## [1] "contr.treatment"
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Genes of Interest
####Statistical Analysis of Edar group (lmfit)
#Run analysis of edar group:
fit1 <- lmFit(v1,design1)
fit1 <- eBayes(fit1)
options(digits=3)
topgenes1=topTable(fit1,coef=3,n=50,sort.by="p")
FC = 2^(fit1$coefficients[,3])
FC = ifelse(FC<1,-1/FC,FC)
finalres=topTable(fit1,coef=3,sort="none",n=Inf)Volcano Plot: Edar Group
Heatmap: Edar Group Before and after Batch Removal
Barchart: Edar Group Before and after Batch Removal
PCA Plot: After Batch Removal
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Statistical Analysis of Fgf group
design2 <- model.matrix(~celltype)
fit2 <- lmFit(v2,design2)
fit2 <- eBayes(fit2)
options(digits=3)
topgenes2=topTable(fit2,coef=2,n=50,sort.by="p")
finalres2=topTable(fit2,coef=2,sort="none",n=Inf)
FC2 = 2^(fit2$coefficients[,2])
FC2 = ifelse(FC2<1,-1/FC2,FC2)Volcano Plot: Fgf Group
Heatmap: Fgf Group
Boxplots: Fgf Group
Provenance:
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] d3heatmap_0.6.1 googleVis_0.5.10 reshape_0.8.5
## [4] knitr_1.11 rgl_0.95.1367 ggplot2_1.0.1
## [7] edgeR_3.12.0 limma_3.26.0 Rsubread_1.20.0
## [10] BiocInstaller_1.20.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.1 magrittr_1.5 MASS_7.3-44
## [4] munsell_0.4.2 colorspace_1.2-6 stringr_1.0.0
## [7] plyr_1.8.3 tools_3.2.1 grid_3.2.1
## [10] gtable_0.1.2 png_0.1-7 htmltools_0.2.6
## [13] yaml_2.1.13 digest_0.6.8 RJSONIO_1.3-0
## [16] RColorBrewer_1.1-2 reshape2_1.4.1 formatR_1.2.1
## [19] htmlwidgets_0.5 base64enc_0.1-3 evaluate_0.8
## [22] rmarkdown_0.8.1 labeling_0.3 stringi_0.5-5
## [25] scales_0.3.0 jsonlite_0.9.17 proto_0.3-10